The Early Detection of Breast Cancer Using Liquid Biopsies: Model Estimates of the Benefits, Harms, and Costs.

Breast cancer screening is associated with harms, such as false-positives and overdiagnoses, and, thus, novel screen tests can be considered. Liquid biopsies have been proposed as a novel method for the early detection of cancer, but low cell-free DNA tumor fraction might pose a problem for the use in population screening. Using breast cancer microsimulation model MISCAN-Fadia, we estimated the outcomes of using liquid biopsies in breast cancer screening in women aged 50 to 74 in the United States. For varying combinations of test sensitivity and specificity, we quantify the impact of the use of liquid biopsies on the harms and benefits of screening, and we estimate the maximum liquid biopsy price for cost-effective implementation in breast cancer screening at a cost-effectiveness threshold of USD 50,000. We investigate under what conditions liquid biopsies could be a suitable alternative to digital mammography and compare these conditions to a CCGA substudy. Outcomes were compared to digital mammography screening, and include mortality reduction, overdiagnoses, quality-adjusted life-years (QALYs), and the maximum price of a liquid biopsy for cost-effective implementation. When liquid biopsies are unable to detect DCIS, a large proportion of overdiagnosed cases is prevented but overall breast cancer mortality reduction and quality of life are lower, and costs are higher compared to digital mammography screening. Liquid biopsies prices should be restricted to USD 187 per liquid biopsy depending on test performance. Overall, liquid biopsies that are unable to detect ductal carcinoma in situ (DCIS) need to be able to detect small, early-stage tumors, with high specificity, at low costs in order to be an alternative to digital mammography. Liquid biopsies might be more suitable as an addition to digital mammography than as an alternative.

Trade-Offs Between Harms and Benefits of Different Breast Cancer Screening Intervals Among Low-Risk Women.

BACKGROUND: A paucity of research addresses breast cancer screening strategies for women at lower-than-average breast cancer risk. The aim of this study was to examine screening harms and benefits among women aged 50-74 years at lower-than-average breast cancer risk by breast density. METHODS: Three well-established, validated Cancer Intervention and Surveillance Network models were used to estimate the lifetime benefits and harms of different screening scenarios, varying by screening interval (biennial, triennial). Breast cancer deaths averted, life-years and quality-adjusted life-years gained, false-positives, benign biopsies, and overdiagnosis were assessed by relative risk (RR) level (0.6, 0.7, 0.85, 1 [average risk]) and breast density category, for US women born in 1970. RESULTS: Screening benefits decreased proportionally with decreasing risk and with lower breast density. False-positives, unnecessary biopsies, and the percentage overdiagnosis also varied substantially by breast density category; false-positives and unnecessary biopsies were highest in the heterogeneously dense category. For women with fatty or scattered fibroglandular breast density and a relative risk of no more than 0.85, the additional deaths averted and life-years gained were small with biennial vs triennial screening. For these groups, undergoing 4 additional screens (screening biennially [13 screens] vs triennially [9 screens]) averted no more than 1 additional breast cancer death and gained no more than 16 life-years and no more than 10 quality-adjusted life-years per 1000 women but resulted in up to 232 more false-positives per 1000 women. CONCLUSION: Triennial screening from age 50 to 74 years may be a reasonable screening strategy for women with lower-than-average breast cancer risk and fatty or scattered fibroglandular breast density.

Personalizing Breast Cancer Screening Based on Polygenic Risk and Family History.

BACKGROUND: We assessed the clinical utility of a first-degree breast cancer family history and polygenic risk score (PRS) to inform screening decisions among women aged 30-50 years. METHODS: Two established breast cancer models evaluated digital mammography screening strategies in the 1985 US birth cohort by risk groups defined by family history and PRS based on 313 single nucleotide polymorphisms. Strategies varied in initiation age (30, 35, 40, 45, and 50 years) and interval (annual, hybrid, biennial, triennial). The benefits (breast cancer deaths averted, life-years gained) and harms (false-positive mammograms, overdiagnoses) were compared with those seen with 3 established screening guidelines. RESULTS: Women with a breast cancer family history who initiated biennial screening at age 40 years (vs 50 years) had a 36% (model range = 29%-40%) increase in life-years gained and 20% (model range = 16%-24%) more breast cancer deaths averted, but 21% (model range = 17%-23%) more overdiagnoses and 63% (model range = 62%-64%) more false positives. Screening tailored to PRS vs biennial screening from 50 to 74 years had smaller positive effects on life-years gained (20%) and breast cancer deaths averted (11%) but also smaller increases in overdiagnoses (10%) and false positives (26%). Combined use of family history and PRS vs biennial screening from 50 to 74 years had the greatest increase in life-years gained (29%) and breast cancer deaths averted (18%). CONCLUSIONS: Our results suggest that breast cancer family history and PRS could guide screening decisions before age 50 years among women at increased risk for breast cancer but expected increases in overdiagnoses and false positives should be expected.

Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches.

BACKGROUND: Ductal carcinoma in situ (DCIS) can be a precursor to invasive breast cancer. Since the advent of screening mammography in the 1980's, the incidence of DCIS has increased dramatically. The value of screen detection and treatment of DCIS, however, is a matter of controversy, as it is unclear the extent to which detection and treatment of DCIS prevents invasive disease and reduces breast cancer mortality. The aim of this paper is to provide an overview of existing Cancer Intervention and Surveillance Modelling Network (CISNET) modeling approaches for the natural history of DCIS, and to compare these to other modeling approaches reported in the literature. DESIGN: Five of the 6 CISNET models currently include DCIS. Most models assume that some, but not all, lesions progress to invasive cancer. The natural history of DCIS cannot be directly observed and the CISNET models differ in their assumptions and in the data sources used to estimate the DCIS model parameters. RESULTS: These model differences translate into variation in outcomes, such as the amount of overdiagnosis of DCIS, with estimates ranging from 34% to 72% for biennial screening from ages 50 to 74 y. The other models described in the literature also report a large range in outcomes, with progression rates varying from 20% to 91%. LIMITATIONS: DCIS grade was not yet included in the CISNET models. CONCLUSION: In the future, DCIS data by grade from active surveillance trials, the development of predictive markers of progression probability, and evidence from other screening modalities, such as tomosynthesis, may be used to inform and improve the models' representation of DCIS, and might lead to convergence of the model estimates. Until then, the CISNET model results consistently show a considerable amount of overdiagnosis of DCIS, supporting the safety and value of observational trials for low-risk DCIS.

Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia.

The MISCAN-Fadia microsimulation model uses continuous tumor growth to simulate the natural history of breast cancer and has been used extensively to estimate the impact of screening and adjuvant treatment on breast cancer incidence and mortality trends. The model simulates individual life histories from birth to death, with and without breast cancer, in the presence and in the absence of screening and treatment. Life histories are simulated according to discrete events such as birth, tumor inception, the tumor's clinical diagnosis diameter in the absence of screening, and death from breast cancer or death from other causes. MISCAN-Fadia consists of 4 main components: demography, natural history of breast cancer, screening, and treatment. Screening impact on the natural history of breast cancer is assessed by simulating continuous tumor growth and the "fatal diameter" concept. This concept implies that tumors diagnosed at a size that is between the screen detection threshold and the fatal diameter are cured, while tumors diagnosed at a diameter larger than the fatal tumor diameter metastasize and lead to breast cancer death. MISCAN-Fadia has been extended by including a different natural history for molecular subtypes based on a tumor's estrogen receptor (ER) status and human epidermal growth factor receptor 2 (HER2) status. In addition, personalized screening strategies that target women based on their risk such as breast density have been incorporated into the model. This personalized approach to screening will continue to develop in light of potential polygenic risk stratification possibilities and new screening modalities.

Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49.

BACKGROUND: The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results. METHODS: Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial. RESULTS: The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial. CONCLUSIONS: The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.

Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology.

BACKGROUND: Collaborative modeling has been used to estimate the impact of potential cancer screening strategies worldwide. A necessary step in the interpretation of collaborative cancer screening model results is to understand how model structure and model assumptions influence cancer incidence and mortality predictions. In this study, we examined the relative contributions of the pre-clinical duration of breast cancer, the sensitivity of screening, and the improvement in prognosis associated with treatment of screen-detected cases to the breast cancer incidence and mortality predictions of 5 Cancer Intervention and Surveillance Modeling Network (CISNET) models. METHODS: To tease out the impact of model structure and assumptions on model predictions, the Maximum Clinical Incidence Reduction (MCLIR) method compares changes in the number of breast cancers diagnosed due to clinical symptoms and cancer mortality between 4 simplified scenarios: 1) no-screening; 2) one-time perfect screening exam, which detects all existing cancers and perfect treatment (i.e., cure) of all screen-detected cancers; 3) one-time digital mammogram and perfect treatment of all screen-detected cancers; and 4) one-time digital mammogram and current guideline-concordant treatment of all screen-detected cancers. RESULTS: The 5 models predicted a large range in maximum clinical incidence (19% to 71%) and in breast cancer mortality reduction (33% to 67%) from a one-time perfect screening test and perfect treatment. In this perfect scenario, the models with assumptions of tumor inception before it is first detectable by mammography predicted substantially higher incidence and mortality reductions than models with assumptions of tumor onset at the start of a cancer's screen-detectable phase. The range across models in breast cancer clinical incidence (11% to 24%) and mortality reduction (8% to 18%) from a one-time digital mammogram at age 62 y with observed sensitivity and current guideline-concordant treatment was considerably smaller than achievable under perfect conditions. CONCLUSIONS: The timing of tumor inception and its effect on the length of the pre-clinical phase of breast cancer had a substantial impact on the grouping of models based on their predictions for clinical incidence and breast cancer mortality reduction. This key finding about the timing of tumor inception will be included in future CISNET breast analyses to enhance model transparency. The MCLIR approach should aid in the interpretation of variations in model results and could be adopted in other disease screening settings to enhance model transparency.

Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012.

Importance: Given recent advances in screening mammography and adjuvant therapy (treatment), quantifying their separate and combined effects on US breast cancer mortality reductions by molecular subtype could guide future decisions to reduce disease burden. Objective: To evaluate the contributions associated with screening and treatment to breast cancer mortality reductions by molecular subtype based on estrogen-receptor (ER) and human epidermal growth factor receptor 2 (ERBB2, formerly HER2 or HER2/neu). Design, Setting, and Participants: Six Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2012 using national data on plain-film and digital mammography patterns and performance, dissemination and efficacy of ER/ERBB2-specific treatment, and competing mortality. Multiple US birth cohorts were simulated. Exposures: Screening mammography and treatment. Main Outcomes and Measures: The models compared age-adjusted, overall, and ER/ERBB2-specific breast cancer mortality rates from 2000 to 2012 for women aged 30 to 79 years relative to the estimated mortality rate in the absence of screening and treatment (baseline rate); mortality reductions were apportioned to screening and treatment. Results: In 2000, the estimated reduction in overall breast cancer mortality rate was 37% (model range, 27%-42%) relative to the estimated baseline rate in 2000 of 64 deaths (model range, 56-73) per 100 000 women: 44% (model range, 35%-60%) of this reduction was associated with screening and 56% (model range, 40%-65%) with treatment. In 2012, the estimated reduction in overall breast cancer mortality rate was 49% (model range, 39%-58%) relative to the estimated baseline rate in 2012 of 63 deaths (model range, 54-73) per 100 000 women: 37% (model range, 26%-51%) of this reduction was associated with screening and 63% (model range, 49%-74%) with treatment. Of the 63% associated with treatment, 31% (model range, 22%-37%) was associated with chemotherapy, 27% (model range, 18%-36%) with hormone therapy, and 4% (model range, 1%-6%) with trastuzumab. The estimated relative contributions associated with screening vs treatment varied by molecular subtype: for ER+/ERBB2-, 36% (model range, 24%-50%) vs 64% (model range, 50%-76%); for ER+/ERBB2+, 31% (model range, 23%-41%) vs 69% (model range, 59%-77%); for ER-/ERBB2+, 40% (model range, 34%-47%) vs 60% (model range, 53%-66%); and for ER-/ERBB2-, 48% (model range, 38%-57%) vs 52% (model range, 44%-62%). Conclusions and Relevance: In this simulation modeling study that projected trends in breast cancer mortality rates among US women, decreases in overall breast cancer mortality from 2000 to 2012 were associated with advances in screening and in adjuvant therapy, although the associations varied by breast cancer molecular subtype.

Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age.

BACKGROUND: There are no publicly available tools designed specifically to assist policy makers to make informed decisions about the optimal ages of breast cancer screening initiation for different populations of US women. OBJECTIVE: To use three established simulation models to develop a web-based tool called Mammo OUTPuT. METHODS: The simulation models use the 1970 US birth cohort and common parameters for incidence, digital screening performance, and treatment effects. Outcomes include breast cancers diagnosed, breast cancer deaths averted, breast cancer mortality reduction, false-positive mammograms, benign biopsies, and overdiagnosis. The Mammo OUTPuT tool displays these outcomes for combinations of age at screening initiation (every year from 40 to 49), annual versus biennial interval, lifetime versus 10-year horizon, and breast density, compared to waiting to start biennial screening at age 50 and continuing to 74. The tool was piloted by decision makers (n = 16) who completed surveys. RESULTS: The tool demonstrates that benefits in the 40s increase linearly with earlier initiation age, without a specific threshold age. Likewise, the harms of screening increase monotonically with earlier ages of initiation in the 40s. The tool also shows users how the balance of benefits and harms varies with breast density. Surveys revealed that 100% of users (16/16) liked the appearance of the site; 94% (15/16) found the tool helpful; and 94% (15/16) would recommend the tool to a colleague. CONCLUSIONS: This tool synthesizes a representative subset of the most current CISNET (Cancer Intervention and Surveillance Modeling Network) simulation model outcomes to provide policy makers with quantitative data on the benefits and harms of screening women in the 40s. Ultimate decisions will depend on program goals, the population served, and informed judgments about the weight of benefits and harms.

Tailoring Breast Cancer Screening Intervals by Breast Density and Risk for Women Aged 50 Years or Older: Collaborative Modeling of Screening Outcomes.

Background: Biennial screening is generally recommended for average-risk women aged 50 to 74 years, but tailored screening may provide greater benefits. Objective: To estimate outcomes for various screening intervals after age 50 years based on breast density and risk for breast cancer. Design: Collaborative simulation modeling using national incidence, breast density, and screening performance data. Setting: United States. Patients: Women aged 50 years or older with various combinations of breast density and relative risk (RR) of 1.0, 1.3, 2.0, or 4.0. Intervention: Annual, biennial, or triennial digital mammography screening from ages 50 to 74 years (vs. no screening) and ages 65 to 74 years (vs. biennial digital mammography from ages 50 to 64 years). Measurements: Lifetime breast cancer deaths, life expectancy and quality-adjusted life-years (QALYs), false-positive mammograms, benign biopsy results, overdiagnosis, cost-effectiveness, and ratio of false-positive results to breast cancer deaths averted. Results: Screening benefits and overdiagnosis increase with breast density and RR. False-positive mammograms and benign results on biopsy decrease with increasing risk. Among women with fatty breasts or scattered fibroglandular density and an RR of 1.0 or 1.3, breast cancer deaths averted were similar for triennial versus biennial screening for both age groups (50 to 74 years, median of 3.4 to 5.1 vs. 4.1 to 6.5 deaths averted; 65 to 74 years, median of 1.5 to 2.1 vs. 1.8 to 2.6 deaths averted). Breast cancer deaths averted increased with annual versus biennial screening for women aged 50 to 74 years at all levels of breast density and an RR of 4.0, and those aged 65 to 74 years with heterogeneously or extremely dense breasts and an RR of 4.0. However, harms were almost 2-fold higher. Triennial screening for the average-risk subgroup and annual screening for the highest-risk subgroup cost less than $100 000 per QALY gained. Limitation: Models did not consider women younger than 50 years, those with an RR less than 1, or other imaging methods. Conclusion: Average-risk women with low breast density undergoing triennial screening and higher-risk women with high breast density receiving annual screening will maintain a similar or better balance of benefits and harms than average-risk women receiving biennial screening. Primary Funding Source: National Cancer Institute.

Radiation-Induced Breast Cancer Incidence and Mortality From Digital Mammography Screening: A Modeling Study.

BACKGROUND: Estimates of risk for radiation-induced breast cancer from mammography screening have not considered variation in dose exposure or diagnostic work-up after abnormal screening results. OBJECTIVE: To estimate distributions of radiation-induced breast cancer incidence and mortality from digital mammography screening while considering exposure from screening and diagnostic mammography and dose variation among women. DESIGN: 2 simulation-modeling approaches. SETTING: U.S. population. PATIENTS: Women aged 40 to 74 years. INTERVENTION: Annual or biennial digital mammography screening from age 40, 45, or 50 years until age 74 years. MEASUREMENTS: Lifetime breast cancer deaths averted (benefits) and radiation-induced breast cancer incidence and mortality (harms) per 100,000 women screened. RESULTS: Annual screening of 100,000 women aged 40 to 74 years was projected to induce 125 breast cancer cases (95% CI, 88 to 178) leading to 16 deaths (CI, 11 to 23), relative to 968 breast cancer deaths averted by early detection from screening. Women exposed at the 95th percentile were projected to develop 246 cases of radiation-induced breast cancer leading to 32 deaths per 100,000 women. Women with large breasts requiring extra views for complete examination (8% of population) were projected to have greater radiation-induced breast cancer risk (266 cancer cases and 35 deaths per 100,000 women) than other women (113 cancer cases and 15 deaths per 100,000 women). Biennial screening starting at age 50 years reduced risk for radiation-induced cancer 5-fold. LIMITATION: Life-years lost from radiation-induced breast cancer could not be estimated. CONCLUSION: Radiation-induced breast cancer incidence and mortality from digital mammography screening are affected by dose variability from screening, resultant diagnostic work-up, initiation age, and screening frequency. Women with large breasts may have a greater risk for radiation-induced breast cancer. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality, U.S. Preventive Services Task Force, National Cancer Institute.

Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.

BACKGROUND: Controversy persists about optimal mammography screening strategies. OBJECTIVE: To evaluate screening outcomes, taking into account advances in mammography and treatment of breast cancer. DESIGN: Collaboration of 6 simulation models using national data on incidence, digital mammography performance, treatment effects, and other-cause mortality. SETTING: United States. PATIENTS: Average-risk U.S. female population and subgroups with varying risk, breast density, or comorbidity. INTERVENTION: Eight strategies differing by age at which screening starts (40, 45, or 50 years) and screening interval (annual, biennial, and hybrid [annual for women in their 40s and biennial thereafter]). All strategies assumed 100% adherence and stopped at age 74 years. MEASUREMENTS: Benefits (breast cancer-specific mortality reduction, breast cancer deaths averted, life-years, and quality-adjusted life-years); number of mammograms used; harms (false-positive results, benign biopsies, and overdiagnosis); and ratios of harms (or use) and benefits (efficiency) per 1000 screens. RESULTS: Biennial strategies were consistently the most efficient for average-risk women. Biennial screening from age 50 to 74 years avoided a median of 7 breast cancer deaths versus no screening; annual screening from age 40 to 74 years avoided an additional 3 deaths, but yielded 1988 more false-positive results and 11 more overdiagnoses per 1000 women screened. Annual screening from age 50 to 74 years was inefficient (similar benefits, but more harms than other strategies). For groups with a 2- to 4-fold increased risk, annual screening from age 40 years had similar harms and benefits as screening average-risk women biennially from 50 to 74 years. For groups with moderate or severe comorbidity, screening could stop at age 66 to 68 years. LIMITATION: Other imaging technologies, polygenic risk, and nonadherence were not considered. CONCLUSION: Biennial screening for breast cancer is efficient for average-risk populations. Decisions about starting ages and intervals will depend on population characteristics and the decision makers' weight given to the harms and benefits of screening. PRIMARY FUNDING SOURCE: National Institutes of Health.

Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts.

BACKGROUND: Many states have laws requiring mammography facilities to tell women with dense breasts and negative results on screening mammography to discuss supplemental screening tests with their providers. The most readily available supplemental screening method is ultrasonography, but little is known about its effectiveness. OBJECTIVE: To evaluate the benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts. DESIGN: Comparative modeling with 3 validated simulation models. DATA SOURCES: Surveillance, Epidemiology, and End Results Program; Breast Cancer Surveillance Consortium; and medical literature. TARGET POPULATION: Contemporary cohort of women eligible for routine screening. TIME HORIZON: Lifetime. PERSPECTIVE: Payer. INTERVENTION: Supplemental ultrasonography screening for women with dense breasts after a negative screening mammography result. OUTCOME MEASURES: Breast cancer deaths averted, quality-adjusted life-years (QALYs) gained, biopsies recommended after a false-positive ultrasonography result, and costs. RESULTS OF BASE-CASE ANALYSIS: Supplemental ultrasonography screening after a negative mammography result for women aged 50 to 74 years with heterogeneously or extremely dense breasts averted 0.36 additional breast cancer deaths (range across models, 0.14 to 0.75), gained 1.7 QALYs (range, 0.9 to 4.7), and resulted in 354 biopsy recommendations after a false-positive ultrasonography result (range, 345 to 421) per 1000 women with dense breasts compared with biennial screening by mammography alone. The cost-effectiveness ratio was $325,000 per QALY gained (range, $112,000 to $766,000). Supplemental ultrasonography screening for only women with extremely dense breasts cost $246,000 per QALY gained (range, $74,000 to $535,000). RESULTS OF SENSITIVITY ANALYSIS: The conclusions were not sensitive to ultrasonography performance characteristics, screening frequency, or starting age. LIMITATION: Provider costs for coordinating supplemental ultrasonography were not considered. CONCLUSION: Supplemental ultrasonography screening for women with dense breasts would substantially increase costs while producing relatively small benefits. PRIMARY FUNDING SOURCE: National Cancer Institute.